IGE: STEM Graduate training in Data Science: solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training

在科学、技术和工程的许多领域收集的数据的数量和类型都迅速增加；这就要求采取新的方法来培养研究生，以满足这些需求。新的数据革命需要一支新的精通数据的劳动力队伍，能够有效地使用数据来回答重要的科学和实际问题，并将结果传达给广大受众。这些数据科学家应该拥有强大的技术和领导技能，以及在由不同和互补的专业知识组成的团队中工作的能力。为了满足这一培训需求，塔夫茨大学获得了国家科学基金会研究生教育创新奖(IGE)，该奖项将测试一种基于项目的学习的创新方法，向研究生教授使用真实数据集解决现实世界问题的数据密集型领域的知识。这种以解决方案为导向、以学生为主导、以团队为基础、计算丰富(至日)的培训环境将提供多学科团队角色扮演以及数据分析和科学交流方面的经验。该项目将评估冬至方法在为在不同机构类型中使用大数据集的一系列领域的教师提供一种适应性方法方面的有效性。随着冬至方法的改进，它将为学生进入劳动力大军铺平道路，使他们拥有强大的数据分析技能，并成为研究团队的有效成员。该项目将回答两个主要的研究问题：(1)至日方法在多大程度上为学生提供了获得21世纪数据密集型知识、技能和态度的机会？以及(2)学生的成绩是否随着时间的推移而不同，在使用冬至教学法的不同课程和不同的STEM学科之间存在差异？该方法由三个部分组成：(1)3D角色扮演，每个学生在三个不同的跨学科团队中扮演团队领导者、协作者和评审员的角色，旨在基于真实的二次数据分析来开发、执行和交流研究项目；(2)反馈反馈，为学生提供经验，向他们的同行发出和接收有效的批评，以加强团队间的沟通；以及(3)数据分析路线图，为有效沟通结果所需的有效数据分析计划和其他文件提供知识构建指南和关键元素的示例。该方法将在弗里德曼营养学院和工程学院的六个数据密集型课程中进行测试和评估，大约270名参与者在参加以冬至为基础的课程之前、期间和之后至少六个月进行监测。将根据成功的组成部分开发教育资源，以指导培训过程的复制，培训冬至方法的教员，并将其传播到其他STEM领域。研究生教育创新(IGE)项目专注于研究生教育的研究。IGE的目标是试验、测试和验证研究生教育的创新方法，并产生将这些方法推广到更广泛社区所需的知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There has been a rapid increase in both the amount and type of data collected in many fields of science, technology and engineering; this requires new approaches to train graduate students to meet these needs. The new data revolution calls for a new data-savvy workforce capable of using data effectively to answer important scientific and practical questions and to communicate the results to a broad audience. These data scientists should have strong technical and leadership skills and the ability to work in teams comprising different and complementary expertise. To meet this training need, the National Science Foundation Innovations of Graduate Education (IGE) award to Tufts University will test an innovative approach to project-based learning, teaching graduate students in data-intensive fields using real datasets to solve real-world problems. This solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training environment will offer multidisciplinary team role-play and experience in data analytics and scientific communication. This project will evaluate the effectiveness of the SOLSTICE approach in providing an adaptable method for instructors in a range of fields that use large datasets at diverse institution types. As the SOLSTICE approach is refined, it will pave the way for students to enter the workforce with strong technical skills in data analytics and be effective members of research teams. The project will answer two overarching research questions: (1) To what extent does the SOLSTICE approach provide opportunities for students to achieve 21st-century data intensive knowledge, skills, and attitudes? and (2) Are there variations in students' achievement over time, across different courses that use the SOLSTICE approach, and across different STEM disciplines? The approach consists of three components: (1) 3D Role Play, in which each student plays the role of Team Lead, Collaborator, and Reviewer in three different interdisciplinary teams aiming to develop, execute and communicate a research project based on authentic secondary data analyses; (2) Feedback on Feedback, which provides students with experience in giving and receiving effective critiques to their peers to strengthen inter-team communication; and (3) Data Analysis Roadmap, which offers knowledge-building guides and examples of key elements for efficient data analysis plans and other documents needed for effective communication of results. The approach will be tested and evaluated in six data-intensive courses in the Friedman School of Nutrition and in the School of Engineering, with approximately 270 participants monitored before, during, and for at least six months after participation in a SOLSTICE-based course. Educational resources will be developed based on successful components to guide replications of the training process, to train faculty in the SOLSTICE approach, and to disseminate it to other STEM fields. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.